Q-CATN synthesizes diffusion-weighted images at arbitrary gradient directions and b-values from structural MRI, and the paper reports improved diffusion parameter maps and fiber tracts over four prior methods on HCP data.
Image Quality Transfer of Diffusion MRI Guided By High-Resolution Structural MRI
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abstract
Prior work on the Image Quality Transfer on Diffusion MRI (dMRI) has shown significant improvement over traditional interpolation methods. However, the difficulty in obtaining ultra-high resolution Diffusion MRI scans poses a problem in training neural networks to obtain high-resolution dMRI scans. Here we hypothesise that the inclusion of structural MRI images, which can be acquired at much higher resolutions, can be used as a guide to obtaining a more accurate high-resolution dMRI output. To test our hypothesis, we have constructed a novel framework that incorporates structural MRI scans together with dMRI to obtain high-resolution dMRI scans. We set up tests which evaluate the validity of our claim through various configurations and compare the performance of our approach against a unimodal approach. Our results show that the inclusion of structural MRI scans do lead to an improvement in high-resolution image prediction when T1w data is incorporated into the model input.
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Q-space Guided Collaborative Attention Translation Network for Flexible Diffusion-Weighted Images Synthesis
Q-CATN synthesizes diffusion-weighted images at arbitrary gradient directions and b-values from structural MRI, and the paper reports improved diffusion parameter maps and fiber tracts over four prior methods on HCP data.